Hybrid feature fusion for classification optimization of short ECG segment in IoT based intelligent healthcare system

With more than 50 million people worldwide at risk of heart disease, early diagnosis of cardiovascular disease is essential. The classification of electrocardiogram (ECG) recordings can diagnose Atrial Fibrillation (AF) and other types of arrhythmia. Short ECG segments recorded from wearable devices in an IoT-based system can further provide continuous abnormality detection. The performance of ECG classification is usually high in normal segments, but much lower in the target classes, i.e., AF and other arrhythmias, which could result from class imbalance and limited feature representation. Deep learning methods have been employed as feature extractors in previous studies. Among them, convolutional neural networks (CNN) can generate rich features in different scales. But CNN may omit precise temporal information such as the duration between R -waves in two QRS waves (RR interval) irregularity, which is insensitive to noise segments. Thus, aiming at improving the classification performance of AF and other classes in short ECG segments, we propose a hybrid feature fusion method integrating the above-mentioned features. The fused features are trained and tested in a support vector machine classifier. The F 1-score results show that our method outperforms not only the same CNN method without feature fusion in all the four classes, which average F 1-score reached 84.3% and classification time per single sample of 0.005 s, but also several state-of-the-art methods, especially in the target classes, which validates the effectiveness of the proposed method. We then further discuss the impact of length on the performance of the proposed method, providing insights into future applications.